Method for modelling hydrodynamic characteristics of multiphase flows using neuronal networks

ABSTRACT

Method intended for real-time modelling, by neural networks, of hydrodynamic characteristics of multiphase flows in transient phase in pipes. In order to specifically take account of the possible flow regimes of fluids in pipes, various neural or “expert” models are formed for several flow regimes (or subregimes) in the whole of the variation range of the hydrodynamic characteristics of the flows (preferably for each one of them), as well as a neural model estimating the probability of belonging of the flows to each flow regime or subregime, knowing some of the characteristics thereof. The probabilities obtained are used for weighting the estimations delivered by each neural model, the result of the weighted sum being then the estimation eventually retained. Applications to various industries and notably for modelling of hydrocarbon flows in pipelines.

FIELD OF THE INVENTION

The present invention relates to a method intended for real-timemodelling, by neural networks, of hydrodynamic characteristics ofmultiphase flows in transient phase in pipes.

The method finds applications notably for modelling of hydrocarbon flowsin pipelines.

BACKGROUND OF THE INVENTION

Transporting hydrocarbons from production sites to treating plantsconstitutes an important link in the petroleum chain. It is a delicatelink because of the complex interactions between the phases forming thetransported effluents. The basic objective for operators is to reach anoptimum productivity under the best safety conditions. They thereforehave to control as best they can the velocity and the temperature so asto avoid unnecessary pressure drops, unwanted deposits andunsteady-state flows. The method that is generally used consists inmodelling in the best possible way the transportation of complexmultiphase streams so as to provide at all times an image of the flowsin the various parts of the production chain, taking into account theprecise constitution of the effluent, the flow rates, the pressures andthe flow regimes.

There are currently various software tools for simulating the transportof complex multiphase streams, allowing to design suitable productionequipments at an early stage.

Patents U.S. Pat. No. 5,550,761, FR-2,756,044 (U.S. Pat. No. 6,028,992)and FR-2,756,045 (U.S. Pat. No. 5,960,187) filed by the applicantnotably describe modelling methods and tools allowing to simulate thetransport of complex multiphase streams on steady or transient flow andcapable of taking into account instability phenomena that occur becauseof the irregular geometry of the formation crossed by the pipe or of thetopography thereof, referred to by specialists as <<terrain slugging >>or (<severe slugging >>.

The simulation tools are as complex as the modelled phenomena. Precisionand performances can only be obtained after a relatively long modellingtime, which is not really compatible with real-time management.

Another approach allowing, alone or in parallel with the above modellingmethods, real-time management of the parameters of a fluid circulationuses neural networks.

It can be reminded that neural networks define a data processing modesimulating the functioning of biological neural systems. In suchnetworks, an element carries out a relatively simple calculation such asa weighted sum of the signals present at its inputs applied to anon-linear function, which determines the state of the output thereof. Alarge number of such elements, interconnected in series and in parallel,is used. Suitable selection of the weighting factors allows the networkto carry out complex functions. Networks known as retropropagationnetworks for example use multiple layers of elements as defined above.Adaptation of such a network to a precise task is carried out by“training” the network on a certain number of examples and by adjustingthe weighting factors for each element to the suitable values. Inputvalues are presented to the network, the output value produced by thenetwork is analysed and the weighting factors are modified so as tominimize in the best possible way the difference between the effectivevalue at the output and the value expected in the example selected.After sufficient training, the network is suited to respond to new inputvalues for which the output value is not known a priori and to produce asuitable output value. In its principle, a neural network worksaccording to a non-linear regression method which is all the moreeffective in relation to conventional methods. Two network types can beused, mainly MLP (Multi Layer Perceptron) networks, or Kohonen networks,well-known to specialists.

Patent EP-1,176,481 filed by the applicant describes a method intendedfor real-time estimation of the flow regime, at any point of a pipewhose structure is defined by a certain number of structure and physicalparameters, of a multiphase fluid stream defined by several physicalquantities and comprising liquid and gas phases. According to thismethod, the flow regime is modelled by forming a non-linear neuralnetwork with an input layer having as many inputs as there are structureparameters and physical quantities, an output layer with as many outputsas there are quantities necessary for estimation of the flow regime, andat least one intermediate layer, by forming a learning base withpredetermined tables connecting various values obtained for the outputdata to the corresponding values of the input data, and by determining,by iterations, weighting factors of the activation function allowing toproperly connect the values in the input and output data tables.

Output data of the neurons is preferably analysed so as to sort, amongthe values of the output data of the neural network, only the pertinentdata to be taken into account for iterative determination of theweighting coefficients of the activation function.

Patent EP-1,217,474 also filed by the applicant describes a methodallowing to construct a module (hydrodynamic or thermodynamic forexample) that it is best suited to fixed operating conditions dependingon the structure of the pipe and on a set of determined physicalquantities (hydrodynamic or thermodynamic quantities for example), withfixed variation ranges for the parameters and the physical quantities.The learning base is adapted to the imposed operating conditions andoptimized neural networks best adjusted to the imposed operatingconditions are generated. In the case, for example, where the module isto be integrated in a general multiphase flow simulation model, bothhydrodynamic and thermodynamic, the model is used to form the learningbase so as to select the set of physical quantities that is best suitedto the operation of the model, as well as the variation ranges fixed forsaid parameters and said physical quantities, and the optimized neuralnetworks that best adjust to the learning base formed are generated.

In the aforementioned prior methods, the flows are considered in aglobal way, without distinction between the various possible flowregimes of the fluids in the pipe: stratified flow, dispersed flow,intermittent flow, whose behaviours are different. This can lead tomodelling errors that are too great in relation to the estimationquality required for production monitoring. Furthermore, they do nottake account of the existence of simple models (for example analyticmodels) translating in mathematical form characteristics of one or moreflow regimes.

SUMMARY OF THE INVENTION

The object of the method according to the invention is the constructionof a model for real-time simulation of the hydrodynamic behaviour of amultiphase fluid flow in transient phase in a pipe, considering fixedoperating conditions concerning a certain number of determined structureparameters relative to the pipe and a set of determined physicalquantities, with fixed variation ranges for said parameters and saidphysical quantities. Neural networks are used with inputs for structureparameters and physical quantities, and outputs where results necessaryfor estimation of the hydrodynamic behaviour are available, and at leastone intermediate layer, the neural networks being determined iterativelyso as to adjust to the values of a learning base with predeterminedtables connecting different values obtained for the output data to thecorresponding values of the input data.

The method comprises:

-   -   constructing several neural networks respectively dedicated to        different fluid flow regimes,    -   constructing a probability neural network suited to evaluate at        all times the probabilities for the flow in the pipe to        correspond respectively to the various flow regimes, and    -   combining the results provided by the various neural networks        weighted by said probabilities.

According to an implementation example, the method comprisesconstructing at least three neural networks respectively dedicated tothe stratified flow regime, the dispersed flow regime and theintermittent flow regime, evaluating the probabilities for the fluidflow in the pipe to correspond respectively to the three flow regimesand linearly combining the results at the outputs of the three dedicatedneural networks by weighting them by said probabilities.

When the available database is sufficiently detailed to distinguishsubregimes within a single flow regime, a probability neural network(RN_(Proba)) suited to evaluate at any time the probabilities for theflow in the pipe to correspond respectively to the various flowsubregimes distinguished in the various flow regimes is constructed andthe results provided by the various neural networks are combined byweighting them by said probabilities.

The estimation results obtained are all the more accurate as:

-   -   a neural model is developed by flow regime or subregime, which        allows to take account of the particularities of the physics        contained in each one of the laws represented, and    -   the continuous and derivable (in the mathematical sense)        connection allowing transition between the various laws is        created by a specific neural or expert network.

Besides, the method retains the capacity of the aforementioned methodsfor performing real-time simulation of the flows, and the resultsobtained benefit from the regularity of the estimation functionobtained.

BRIEF DESCRIPTION OF THE FIGURES

Other features and advantages of the method according to the inventionwill be clear from reading the description hereafter of a non-limitativeembodiment example, with reference to the accompanying drawings wherein:

FIG. 1 shows a model structure example, and

FIG. 2 shows an example of the structure of each neural network of FIG.1.

DETAILED DESCRIPTION

We consider a circulation of multiphase fluids in a pipe with at least aliquid phase and at least a gas phase, and we try to construct a modelallowing, from a certain number of geometrical and physical input datarelative to the pipe and of physical data relative to the fluids, togive instantly, for each section of the fluid stream, an estimation ofthe flow regime. We therefore use, as mentioned above, for a givenquantity S (FIG. 1), various neural networks specifically suited tovarious flow regimes N_(flows) in the pipe. We construct for example anexpert network E_(Stra) modelling specifically the stratified flows,another one, E_(Int) modelling specifically the intermittent flows and athird one, E_(Disp), modelling specifically the dispersed flows. We alsoconstruct a neural model RN_(Proba) specifically intended to evaluate atany time the probability p_(Stra), p_(Int) and p_(Disp). If S_(Stra),S_(Int), and S_(Disp) are respectively the output values of the threeexperts, we then construct an evaluation function Ŝ such that:Ŝ=p _(Stra) S _(Stra) +p _(Disp) S _(Disp) +p _(Int) S _(Int).

Inputs and Outputs of the Various Neural or Expert Models Forming theHydrodynamic Model

Whatever the neural model considered, the input data result from:

-   -   geometrical data: diameter of the pipe, roughness, inclination,        etc.,    -   data describing the characteristics of the fluid: density of the        phases, viscosity of the phases, etc.,    -   data characterizing the mixture: gas fraction, gas/liquid        surface tension, etc.,    -   linear or non-linear combinations of these inputs,    -   and also simplified models, continuous or not, containing        information on the physics of the mixture.

Each model produces for example at the output the hydrodynamic behaviourof the effluents, and notably the flow regime. It evaluates and deliversat two main outputs hydrodynamic data in the part of the pipe where theflow regime is to be determined, the velocity difference dV between gasand liquid for example, the linear pressure drop ∂P/∂x or the fraction β(β ε [0; 1]) of flow of the regime processed thereby. Other quantitiesqualifying the flow regime can be calculated from these two outputs.

The outputs provided by the experts are essentially the velocitydifferences between the phases, under the assumption of a certain flowregime (for example, the Stratified expert delivers the estimation ofthe velocity difference between the phases under the assumption of astratified flow).

The outputs provided by the probability network are the probability ofbelonging to each flow regime processed by the expert networks, knowingthe inputs.

Structure of the Networks

The various neural or expert networks dedicated to the different flowregimes are preferably Multi Layer Perceptron (MLP) type networkswell-known to the man skilled in the art, generally estimating ahydrodynamic quantity. They comprise each (FIG. 2) an input layerconsisting of a certain number N_(i) of neurons corresponding to theN_(i) input data of the complete physical model, an output layer of aneuron for example corresponding to the parameter sought (dV, ∂P/∂x orβ), and at least one intermediate layer, referred to as hidden layer,whose number of neurons N_(c) is optimized. The number of hidden layersand the number of neurons which constitute them are determined from thenetwork learning and validation results. The network is totallyconnected. The non-linearity of this network is obtained by either asigmoid activation function governing the behaviour of the neurons inthe hidden layer, or by the identity function or the softmax functionfor the output layer.

The neural networks comprise an input layer, one or two hidden layersand an output layer. The activation functions of the various neurons,well-known to the man skilled in the art, are either the sigmoidfunction (for the hidden layers), or the identity function or thesoftmax function (for the output layers).

Learning

The weights of each network or expert are determined at the end of alearning stage; during this stage, the networks are supplied with a setof data forming their learning base, and the configuration and theweights of the network are optimized by minimizing errors observed forall the samples of the base, between the output data resulting fromnetwork calculation and the data expected at the output, given by thebase. The errors can be the absolute errors between the input and outputquantities or the relative errors, according to the performance desiredfor the network. The generalization capacities of the network are thentested from its capacity to properly calculate the two outputs forinputs that are unknown thereto.

The databases used are of different natures:

-   -   for estimation of the velocity difference dV or of the pressure        drop, each base contains pairs of input/output values, each        output value being the desired value of the estimated quantity        in the case of the flow regime processed by the dedicated        network,    -   for probability estimation, the desired output is a vector of        magnitude equal to the number N_(flows) of flow regimes        considered (in the example of FIG. 1, the vector is of dimension        3); this vector contains (N_(flows)−1) zero values, and a value        equal to 1, which corresponds to the probability for the fluid        flow regime in the pipe to correspond to the flow regime        processed by the dedicated neural network.

In the above example, we have considered three different flow regimes:stratified, intermittent and dispersed. This is in no way limitative. Incases where more detailed data is available, allowing to makedistinctions within a single flow regime, for example to separate wavystratified from smooth stratified within the stratified flow regime,specific experts modelling each one of these subregimes are preferablycreated.

Results

By implementing such modelling, we obtain a continuous and infinitelyderivable transient hydrodynamic model which carries out real-timecalculation of the main hydrodynamic quantities characterizing the flow.The probability estimation function allows to create an overallhydrodynamic law from the different flow laws modelled by the variousdedicated neural models. The transition between two flow laws is more orless steep (more or less great derivative) depending on the precisiongiven to the probability estimation, but it is continuous, whicheliminates possible uncertainties in the model results related to theexistence of discontinuities. The overall model is suited for either useindependently of any other module or integration in a complete model.

1. A method intended for real-time modelling of the hydrodynamic behaviour of a multiphase fluid flow in transient phase in a pipe, considering fixed operating conditions concerning a certain number of determined structure parameters relative to the pipe and a set of determined physical quantities, with fixed variation ranges for said parameters and said physical quantities, by neural networks with inputs for structure parameters and physical quantities, and outputs where results necessary for estimation of the hydrodynamic behaviour are available, and at least one intermediate layer, the neural networks being determined iteratively so as to adjust to the values of a learning base with predetermined tables connecting different values obtained for the output data to the corresponding values of the input data, characterized in that it comprises: constructing several neural networks (E_(Stra), E_(Disp), E_(Int)) respectively dedicated to different fluid flow regimes, constructing a probability neural network (RN_(Proba)) suited to evaluate at all times the probabilities for the flow in the pipe to correspond respectively to the various flow regimes, and combining the results provided by the various neural networks weighted by said probabilities.
 2. A method as claimed in claim 1, characterized in that at least three neural networks respectively dedicated to the stratified flow regime, the dispersed flow regime and the intermittent flow regime are constructed, the probabilities for the fluid flow in the pipe to correspond respectively to the three flow regimes are evaluated and the results at the outputs of the three dedicated neural networks are linearly combined by weighting them by said probabilities.
 3. A method as claimed in claim 1, characterized in that, when the available database is sufficiently detailed to distinguish subregimes within a single flow regime, a probability neural network (RN_(Proba)) suited to evaluate at any time the probabilities for the flow in the pipe to correspond respectively to the various flow subregimes distinguished in the various flow regimes is constructed and the results provided by the various neural networks are combined by weighting them by said probabilities.
 4. A method as claimed in claim 2, characterized in that, when the available database is sufficiently detailed to distinguish subregimes within a single flow regime, a probability neural network (RN_(Proba)) suited to evaluate at any time the probabilities for the flow in the pipe to correspond respectively to the various flow subregimes distinguished in the various flow regimes is constructed and the results provided by the various neural networks are combined by weighting them by said probabilities. 